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Non-compartmental estimation of pharmacokinetic parameters for flexible sampling designs

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Non-compartmental estimation of pharmacokinetic parameters for flexible sampling designs. / Jaki, Thomas; Wolfsegger, Martin J.

In: Statistics in Medicine, Vol. 31, No. 11-12, 2012, p. 1059-1073.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Jaki, T & Wolfsegger, MJ 2012, 'Non-compartmental estimation of pharmacokinetic parameters for flexible sampling designs', Statistics in Medicine, vol. 31, no. 11-12, pp. 1059-1073. https://doi.org/10.1002/sim.4386

APA

Vancouver

Jaki T, Wolfsegger MJ. Non-compartmental estimation of pharmacokinetic parameters for flexible sampling designs. Statistics in Medicine. 2012;31(11-12):1059-1073. doi: 10.1002/sim.4386

Author

Jaki, Thomas ; Wolfsegger, Martin J. / Non-compartmental estimation of pharmacokinetic parameters for flexible sampling designs. In: Statistics in Medicine. 2012 ; Vol. 31, No. 11-12. pp. 1059-1073.

Bibtex

@article{e5e3b2fd22894d379eecceed8cdad981,
title = "Non-compartmental estimation of pharmacokinetic parameters for flexible sampling designs",
abstract = "Pharmacokinetic (PK) studies aim to understand the kinetics of absorption, distribution, metabolism and elimination of a drug. Typically, such studies involve measuring the concentration of the drug in the plasma or blood at several time points after drug administration. In studying the PK behaviour, either the non-compartmental approach or alternatively a modelling approach can be utilized. Traditionally, the non-compartmental approach makes minimal assumptions about the data-generating process but requires the data to be collected in a very structured way. Conversely, the modelling approach depends heavily on assumptions about the data-generating process but does not impose a specific data structure. In this paper, we will discuss non-compartmental methods for estimating the area under the concentration versus time curve and other common PK parameters that use minimal assumptions about the data structure making it applicable to a wide range of PK studies. We will evaluate the methods using simulation and give an illustrative example. ",
keywords = "area under the concentration time curve , AUC , non-compartmental , PK parameters , sparse sampling",
author = "Thomas Jaki and Wolfsegger, {Martin J.}",
year = "2012",
doi = "10.1002/sim.4386",
language = "English",
volume = "31",
pages = "1059--1073",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "11-12",

}

RIS

TY - JOUR

T1 - Non-compartmental estimation of pharmacokinetic parameters for flexible sampling designs

AU - Jaki, Thomas

AU - Wolfsegger, Martin J.

PY - 2012

Y1 - 2012

N2 - Pharmacokinetic (PK) studies aim to understand the kinetics of absorption, distribution, metabolism and elimination of a drug. Typically, such studies involve measuring the concentration of the drug in the plasma or blood at several time points after drug administration. In studying the PK behaviour, either the non-compartmental approach or alternatively a modelling approach can be utilized. Traditionally, the non-compartmental approach makes minimal assumptions about the data-generating process but requires the data to be collected in a very structured way. Conversely, the modelling approach depends heavily on assumptions about the data-generating process but does not impose a specific data structure. In this paper, we will discuss non-compartmental methods for estimating the area under the concentration versus time curve and other common PK parameters that use minimal assumptions about the data structure making it applicable to a wide range of PK studies. We will evaluate the methods using simulation and give an illustrative example.

AB - Pharmacokinetic (PK) studies aim to understand the kinetics of absorption, distribution, metabolism and elimination of a drug. Typically, such studies involve measuring the concentration of the drug in the plasma or blood at several time points after drug administration. In studying the PK behaviour, either the non-compartmental approach or alternatively a modelling approach can be utilized. Traditionally, the non-compartmental approach makes minimal assumptions about the data-generating process but requires the data to be collected in a very structured way. Conversely, the modelling approach depends heavily on assumptions about the data-generating process but does not impose a specific data structure. In this paper, we will discuss non-compartmental methods for estimating the area under the concentration versus time curve and other common PK parameters that use minimal assumptions about the data structure making it applicable to a wide range of PK studies. We will evaluate the methods using simulation and give an illustrative example.

KW - area under the concentration time curve

KW - AUC

KW - non-compartmental

KW - PK parameters

KW - sparse sampling

U2 - 10.1002/sim.4386

DO - 10.1002/sim.4386

M3 - Journal article

VL - 31

SP - 1059

EP - 1073

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 11-12

ER -